{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0aee030d-2bf1-495e-bd87-1b376420514e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts.charts import *\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.commons.utils import JsCode\n",
    "from pyecharts.globals import WarningType\n",
    "from sklearn.feature_selection import SelectKBest, SelectPercentile, SelectFromModel, chi2, f_classif\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz, plot_tree\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, precision_score\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0ff553-1cfe-4b79-9dd0-4583f03956fd",
   "metadata": {},
   "source": [
    "### 读取淘宝用户行为数据集，将交互行为时间拆分成日期。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa364fcb-206f-4187-a511-188d6a2e21a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>user_geohash</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98047837</td>\n",
       "      <td>232431562</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4245</td>\n",
       "      <td>2014-12-06 02</td>\n",
       "      <td>2014-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97726136</td>\n",
       "      <td>383583590</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5894</td>\n",
       "      <td>2014-12-09 20</td>\n",
       "      <td>2014-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98607707</td>\n",
       "      <td>64749712</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2883</td>\n",
       "      <td>2014-12-18 11</td>\n",
       "      <td>2014-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98662432</td>\n",
       "      <td>320593836</td>\n",
       "      <td>1</td>\n",
       "      <td>96nn52n</td>\n",
       "      <td>6562</td>\n",
       "      <td>2014-12-06 10</td>\n",
       "      <td>2014-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21</td>\n",
       "      <td>2014-12-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    item_id  behavior_type user_geohash  item_category  \\\n",
       "0  98047837  232431562              1          NaN           4245   \n",
       "1  97726136  383583590              1          NaN           5894   \n",
       "2  98607707   64749712              1          NaN           2883   \n",
       "3  98662432  320593836              1      96nn52n           6562   \n",
       "4  98145908  290208520              1          NaN          13926   \n",
       "\n",
       "            time       date  \n",
       "0  2014-12-06 02 2014-12-06  \n",
       "1  2014-12-09 20 2014-12-09  \n",
       "2  2014-12-18 11 2014-12-18  \n",
       "3  2014-12-06 10 2014-12-06  \n",
       "4  2014-12-16 21 2014-12-16  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"tianchi_mobile_recommend_train_user.csv\")\n",
    "df[\"date\"] = pd.to_datetime(df[\"time\"].str.slice(stop=10))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363c81ba-2ca1-4658-88f4-96eb4d6bd5df",
   "metadata": {},
   "source": [
    "# 1.构造模型变量\n",
    "### 获取数据集中去重后的所有用户标识，并添加变量y作为目标变量，将用户在最后一天（12月18日）是否有购买行为，初始化为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7537a44-2827-4487-ad3a-aceac04bbd9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98047837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97726136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98607707</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98662432</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98145908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  y\n",
       "0  98047837  0\n",
       "1  97726136  0\n",
       "2  98607707  0\n",
       "3  98662432  0\n",
       "4  98145908  0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "driver = pd.DataFrame({\"user_id\" : df[\"user_id\"].drop_duplicates(), \"y\" : 0})#.set_index(\"user_id\")\n",
    "driver.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ff35e6-8060-42a8-9fac-52bf6a14e108",
   "metadata": {},
   "source": [
    "### 将12月18日有购买行为的用户的目标变量y设为1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba68197e-56fb-489f-a3e6-b9e37cefb92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98047837</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>98607707</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>98662432</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>98145908</th>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "          y\n",
       "user_id    \n",
       "98047837  0\n",
       "97726136  0\n",
       "98607707  1\n",
       "98662432  0\n",
       "98145908  0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy_users = df.loc[(df[\"behavior_type\"]==4) & (df[\"date\"]==\"2014-12-18\"), \"user_id\"].drop_duplicates()\n",
    "driver.loc[driver[\"user_id\"].isin(buy_users), \"y\"] = 1\n",
    "driver = driver.set_index(\"user_id\")\n",
    "driver.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d951c0b-706e-4e2a-a6c7-94663d1c988d",
   "metadata": {},
   "source": [
    "### 创建预测变量\n",
    "### 过去1天、3天、7天和30天内的浏览量、收藏量、加购物车量和购买量，共16个预测变量；\n",
    "### 最近一次浏览、收藏、加购物车和购买行为距离当前的天数，共4个预测变量；\n",
    "### 过去周的同一天的浏览量、收藏量、加购物车量和购买量，用于体现周行为模式，共4个预测变量."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed674111-5c44-4205-9bef-7c857f09855c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>pv_d1</th>\n",
       "      <th>pv_d3</th>\n",
       "      <th>pv_d7</th>\n",
       "      <th>pv_d30</th>\n",
       "      <th>pv_recency</th>\n",
       "      <th>pv_weekly</th>\n",
       "      <th>favor_d1</th>\n",
       "      <th>favor_d3</th>\n",
       "      <th>favor_d7</th>\n",
       "      <th>...</th>\n",
       "      <th>cart_d7</th>\n",
       "      <th>cart_d30</th>\n",
       "      <th>cart_recency</th>\n",
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       "      <th>buy_d1</th>\n",
       "      <th>buy_d3</th>\n",
       "      <th>buy_d7</th>\n",
       "      <th>buy_d30</th>\n",
       "      <th>buy_recency</th>\n",
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       "      <th>98047837</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>97726136</th>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98607707</th>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>98145908</th>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y  pv_d1  pv_d3  pv_d7  pv_d30  pv_recency  pv_weekly  favor_d1  \\\n",
       "user_id                                                                     \n",
       "98047837  0    7.0   33.0  457.0  1662.0         1.0      195.0       NaN   \n",
       "97726136  0   71.0  125.0  265.0   656.0         1.0      105.0       3.0   \n",
       "98607707  1   68.0  133.0  184.0  1437.0         1.0      147.0       NaN   \n",
       "98662432  0    NaN    2.0   16.0    40.0         4.0        7.0       NaN   \n",
       "98145908  0   32.0   36.0   99.0   166.0         2.0        5.0       NaN   \n",
       "\n",
       "          favor_d3  favor_d7  ...  cart_d7  cart_d30  cart_recency  \\\n",
       "user_id                       ...                                    \n",
       "98047837       NaN      10.0  ...      7.0      24.0           5.0   \n",
       "97726136       3.0       5.0  ...      3.0       3.0           7.0   \n",
       "98607707       2.0       3.0  ...      2.0      11.0           2.0   \n",
       "98662432       NaN       NaN  ...      NaN       NaN           NaN   \n",
       "98145908       NaN       NaN  ...      NaN       2.0          15.0   \n",
       "\n",
       "          cart_weekly  buy_d1  buy_d3  buy_d7  buy_d30  buy_recency  \\\n",
       "user_id                                                               \n",
       "98047837          NaN     NaN     NaN     3.0      7.0          6.0   \n",
       "97726136          3.0     NaN     NaN     3.0      6.0          6.0   \n",
       "98607707          NaN     NaN     2.0     3.0     23.0          3.0   \n",
       "98662432          NaN     NaN     NaN     1.0      1.0          7.0   \n",
       "98145908          NaN     1.0     1.0     1.0      2.0          2.0   \n",
       "\n",
       "          buy_weekly  \n",
       "user_id               \n",
       "98047837         NaN  \n",
       "97726136         1.0  \n",
       "98607707         2.0  \n",
       "98662432         1.0  \n",
       "98145908         NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_type_dict = {1:\"pv\", 2:\"favor\", 3:\"cart\", 4:\"buy\"}\n",
    "for behavior_key, behavior_name in behavior_type_dict.items():\n",
    "    # frequency\n",
    "    for past_days in [1,3,7,30]:\n",
    "        count_by_user = (\n",
    "            df[(df[\"behavior_type\"]==behavior_key) & (df[\"date\"].between(pd.to_datetime(\"2014-12-17\") - pd.DateOffset(days=past_days), \"2014-12-17\"))].\n",
    "            groupby(\"user_id\")[\"user_id\"].count()\n",
    "        )\n",
    "        driver = driver.merge(count_by_user, \"left\", left_index=True, right_index=True)\n",
    "        driver.columns = driver.columns[:-1].tolist() + [behavior_name+\"_d\"+str(past_days)]\n",
    "    # recency\n",
    "    recency_by_user = (\n",
    "        pd.to_datetime(\"2014-12-18\") - \n",
    "        df[(df[\"behavior_type\"]==behavior_key) & (df[\"date\"]<=\"2014-12-17\")].groupby(\"user_id\")[\"date\"].max()\n",
    "    ).dt.days\n",
    "    driver = driver.merge(recency_by_user, \"left\", left_index=True, right_index=True)\n",
    "    driver.columns = driver.columns[:-1].tolist() + [behavior_name+\"_recency\"]\n",
    "    # weekly\n",
    "    weekly_by_user = (\n",
    "        df[(df[\"behavior_type\"]==behavior_key) & (df[\"date\"]<=\"2014-12-17\") & ((pd.to_datetime(\"2014-12-18\") - df[\"date\"]).dt.days % 7 == 0)].\n",
    "        groupby(\"user_id\")[\"user_id\"].count()\n",
    "    )\n",
    "    driver = driver.merge(weekly_by_user, \"left\", left_index=True, right_index=True)\n",
    "    driver.columns = driver.columns[:-1].tolist() + [behavior_name+\"_weekly\"]   \n",
    "\n",
    "driver.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e468f6-6122-439d-98ee-ab94545b76c3",
   "metadata": {},
   "source": [
    "## 数据填充\n",
    "### 对于4个表示行为距离当前天数的预测变量，空值表示历史上没有相关行为，这里用999填充；\r",
    "### \n",
    "对于其他表示行为频次的预测变量，空置表示历史上没有相关行为这里以填充为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58ffe9c7-e2bf-4b70-b051-33f03628cc49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>pv_d1</th>\n",
       "      <th>pv_d3</th>\n",
       "      <th>pv_d7</th>\n",
       "      <th>pv_d30</th>\n",
       "      <th>pv_recency</th>\n",
       "      <th>pv_weekly</th>\n",
       "      <th>favor_d1</th>\n",
       "      <th>favor_d3</th>\n",
       "      <th>favor_d7</th>\n",
       "      <th>...</th>\n",
       "      <th>cart_d7</th>\n",
       "      <th>cart_d30</th>\n",
       "      <th>cart_recency</th>\n",
       "      <th>cart_weekly</th>\n",
       "      <th>buy_d1</th>\n",
       "      <th>buy_d3</th>\n",
       "      <th>buy_d7</th>\n",
       "      <th>buy_d30</th>\n",
       "      <th>buy_recency</th>\n",
       "      <th>buy_weekly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98047837</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97726136</th>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98607707</th>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98662432</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98145908</th>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y  pv_d1  pv_d3  pv_d7  pv_d30  pv_recency  pv_weekly  favor_d1  \\\n",
       "user_id                                                                     \n",
       "98047837  0    7.0   33.0  457.0  1662.0         1.0      195.0       0.0   \n",
       "97726136  0   71.0  125.0  265.0   656.0         1.0      105.0       3.0   \n",
       "98607707  1   68.0  133.0  184.0  1437.0         1.0      147.0       0.0   \n",
       "98662432  0    0.0    2.0   16.0    40.0         4.0        7.0       0.0   \n",
       "98145908  0   32.0   36.0   99.0   166.0         2.0        5.0       0.0   \n",
       "\n",
       "          favor_d3  favor_d7  ...  cart_d7  cart_d30  cart_recency  \\\n",
       "user_id                       ...                                    \n",
       "98047837       0.0      10.0  ...      7.0      24.0           5.0   \n",
       "97726136       3.0       5.0  ...      3.0       3.0           7.0   \n",
       "98607707       2.0       3.0  ...      2.0      11.0           2.0   \n",
       "98662432       0.0       0.0  ...      0.0       0.0         999.0   \n",
       "98145908       0.0       0.0  ...      0.0       2.0          15.0   \n",
       "\n",
       "          cart_weekly  buy_d1  buy_d3  buy_d7  buy_d30  buy_recency  \\\n",
       "user_id                                                               \n",
       "98047837          0.0     0.0     0.0     3.0      7.0          6.0   \n",
       "97726136          3.0     0.0     0.0     3.0      6.0          6.0   \n",
       "98607707          0.0     0.0     2.0     3.0     23.0          3.0   \n",
       "98662432          0.0     0.0     0.0     1.0      1.0          7.0   \n",
       "98145908          0.0     1.0     1.0     1.0      2.0          2.0   \n",
       "\n",
       "          buy_weekly  \n",
       "user_id               \n",
       "98047837         0.0  \n",
       "97726136         1.0  \n",
       "98607707         2.0  \n",
       "98662432         1.0  \n",
       "98145908         0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "driver[[col for col in driver.columns if \"recency\" in col]] = driver[[col for col in driver.columns if \"recency\" in col]].fillna(999)\n",
    "driver=driver.fillna(0)\n",
    "driver.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5970d831-4c6d-483a-8ed5-57dd6956f880",
   "metadata": {},
   "source": [
    "## 创建特征选择器，计算每个预测变量对于目标变量的卡方值，即预测变量的预测能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65855acb-7493-4ba7-8e7c-1998d981b9ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pv_d30</td>\n",
       "      <td>409931.868482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pv_d7</td>\n",
       "      <td>165899.932290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>buy_recency</td>\n",
       "      <td>125768.942622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pv_d3</td>\n",
       "      <td>103396.907910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>cart_recency</td>\n",
       "      <td>96222.842562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pv_d1</td>\n",
       "      <td>72383.144128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pv_weekly</td>\n",
       "      <td>54243.782035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cart_d30</td>\n",
       "      <td>35118.440462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>favor_recency</td>\n",
       "      <td>24499.478325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>buy_d30</td>\n",
       "      <td>14141.622666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cart_d7</td>\n",
       "      <td>12589.410105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cart_d3</td>\n",
       "      <td>7441.615784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>favor_d30</td>\n",
       "      <td>6030.017449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>buy_d7</td>\n",
       "      <td>5134.362634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>cart_weekly</td>\n",
       "      <td>5083.714845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>cart_d1</td>\n",
       "      <td>5053.577719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>favor_d7</td>\n",
       "      <td>2469.969455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>buy_d3</td>\n",
       "      <td>2147.807138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>buy_weekly</td>\n",
       "      <td>2129.611630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>favor_d3</td>\n",
       "      <td>1688.478902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>favor_d1</td>\n",
       "      <td>1494.984759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>buy_d1</td>\n",
       "      <td>1381.596229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>favor_weekly</td>\n",
       "      <td>717.236924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pv_recency</td>\n",
       "      <td>125.154219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          feature          score\n",
       "3          pv_d30  409931.868482\n",
       "2           pv_d7  165899.932290\n",
       "22    buy_recency  125768.942622\n",
       "1           pv_d3  103396.907910\n",
       "16   cart_recency   96222.842562\n",
       "0           pv_d1   72383.144128\n",
       "5       pv_weekly   54243.782035\n",
       "15       cart_d30   35118.440462\n",
       "10  favor_recency   24499.478325\n",
       "21        buy_d30   14141.622666\n",
       "14        cart_d7   12589.410105\n",
       "13        cart_d3    7441.615784\n",
       "9       favor_d30    6030.017449\n",
       "20         buy_d7    5134.362634\n",
       "17    cart_weekly    5083.714845\n",
       "12        cart_d1    5053.577719\n",
       "8        favor_d7    2469.969455\n",
       "19         buy_d3    2147.807138\n",
       "23     buy_weekly    2129.611630\n",
       "7        favor_d3    1688.478902\n",
       "6        favor_d1    1494.984759\n",
       "18         buy_d1    1381.596229\n",
       "11   favor_weekly     717.236924\n",
       "4      pv_recency     125.154219"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skb = SelectKBest(chi2)\n",
    "skb.fit(driver.iloc[:,1:], driver[\"y\"])\n",
    "scores = pd.DataFrame({'feature': driver.columns[1:], 'score': skb.scores_})\n",
    "scores.sort_values(\"score\", ascending = False, inplace=True)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d7c194-8d79-4760-93be-3913fc56179b",
   "metadata": {},
   "source": [
    "## 绘制预测变量的卡方值柱状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9c2aab9-95eb-4b1d-bf19-e18240ee1e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,5))\n",
    "plt.bar(np.arange(24), scores[\"score\"], align=\"center\", alpha=0.5)\n",
    "plt.xticks(np.arange(24), scores[\"feature\"], rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0758e29d-d4bf-425b-96cf-279147c81311",
   "metadata": {},
   "source": [
    "# 2.建立决策树模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c24784-75fa-4df6-85eb-e38344aa7b78",
   "metadata": {},
   "source": [
    "## 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb6f4ae6-fc91-4866-97cd-38d24395995c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(driver.iloc[:,1:], driver[\"y\"], test_size = 0.3, random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4c33c2-4817-495a-9fb7-d65edabd81c0",
   "metadata": {},
   "source": [
    "## 定义决策树分类模型，在训练集上做超参数调优。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e767094d-f6a4-4b42-80db-b0b499bc581f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, min_impurity_decrease=0.001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, min_impurity_decrease=0.001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_impurity_decrease=0.001)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "param_grid = {'max_depth': [3,4,5,6,7,8], 'min_impurity_decrease': [0.001,0.005,0.01]}\n",
    "gs = GridSearchCV(clf,param_grid,scoring = \"roc_auc\", cv = 3)\n",
    "gs.fit(train_X, train_y)\n",
    "cv_results = pd.DataFrame(gs.cv_results_)\n",
    "gs.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35fa4b1a-7641-421c-87a7-a60b5e97d60b",
   "metadata": {},
   "source": [
    "## 可视化超参数调优结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc1ead31-7da3-4740-a492-71595eaf02fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "grouped = cv_results.groupby('param_min_impurity_decrease')\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=ax, x='param_max_depth', y='mean_test_score', label=key)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a1f19a-ecd0-44ef-a743-cc8a04158270",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4648fafd-314e-46ff-b13d-8e1f3238f772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, min_impurity_decrease=0.001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, min_impurity_decrease=0.001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_impurity_decrease=0.001)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = gs.best_estimator_\n",
    "clf.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a628109-005b-40e4-84fe-95274f4202dd",
   "metadata": {},
   "source": [
    "## 可视化决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b8c7e3c2-adcb-4fea-b567-663c0ee7a74d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "plot_tree(clf, max_depth=3, feature_names=driver.columns[1:], filled=True, rounded=True, fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c60182-cdd0-4434-b582-2fb76d7a53ab",
   "metadata": {},
   "source": [
    "## 模型测试，得到模型精度和ROC曲线下面积。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f655d89b-2e80-4c5e-b91c-dba475a4880e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446666666666667"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_y = clf.predict(test_X)\n",
    "probas_y = clf.predict_proba(test_X)\n",
    "accuracy_score(test_y, predict_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cff7038e-3928-4f9d-bd02-50a7aa2fb6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6707364290297411"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(test_y, probas_y[:, 1])"
   ]
  }
 ],
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